Debugging
Import Data
corrcounts_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/corrcounts_merge.rds")
metadata_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/metadata_merge.rds")
SenescenceSignatures <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_divided_newCellAge.RDS")set.seed("123456")
metadata_merge_corr <- metadata_merge
metadata_merge_corr$random_cat <- sample(c("A","B","C"), nrow(metadata_merge_corr), replace = T)
metadata_merge_corr$random_numeric <- sample(0:100, nrow(metadata_merge_corr), replace = TRUE)
metadata_merge_corr$Is_Senescent <- ifelse(metadata_merge_corr$Condition == "Senescent", "Senescent", "Non Senescent")
library(markeR)
library(ggplot2)
library(ggpubr)
library(edgeR)
?markeRScores
?CalculateScores
ℹ Rendering development documentation for "CalculateScores"Unidirectional
df_ssGSEA <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = SenescenceSignatures)Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variablessenescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ssGSEA, ColorVariable = "CellType", GroupingVariable="Condition", method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,cond_cohend=cond_cohend)
df_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = SenescenceSignatures)
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition", method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = SenescenceSignatures)Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Considering unidirectional gene signature mode for signature [UP]_CellAge
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYOsenescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition", method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
plotlist <- list()
for (sig in names(df_ssGSEA)){
df_subset_ssGSEA <- df_ssGSEA[[sig]]
df_subset_logmedian <- df_logmedian[[sig]]
df_subset_merge <- merge(df_subset_ssGSEA,df_subset_logmedian,by="sample")
# Wrap the signature name using the helper function
wrapped_title <- wrap_title_aux(sig, width = 20)
plotlist[[sig]] <- ggplot2::ggplot(df_subset_merge, aes(x=score.x, y=score.y)) +
geom_point(size=4, alpha=0.8, fill="darkgrey", shape=21) +
theme_bw() +
xlab("ssGSEA Enrichment Score") +
ylab("Normalised Signature Score") +
ggtitle(wrapped_title) +
theme(plot.title = ggplot2::element_text(hjust = 0.5, size=10),
plot.subtitle = ggplot2::element_text(hjust = 0.5))
}
ggpubr::ggarrange(plotlist=plotlist, nrow=3, ncol=4, align = "h")Bidirectional gene signatures
Try scores with bidirectional signatures
bidirectsigs <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_complete_newCellAge.RDS")
for (sig in names(bidirectsigs)){
sigdf <- bidirectsigs[[sig]]
sigdf <- sigdf[,1:2] # remove the third column, if applicable
if(any(sigdf[,2]=="not_reported")){
sigdf <- sigdf[,1]
bidirectsigs[[sig]] <- sigdf
next
}
sigdf[,2] <- ifelse(sigdf[,2]=="enriched",1,-1)
bidirectsigs[[sig]] <- sigdf
}
bidirectsigs$CellAge
$CSgene
[1] "TP53" "TERF2" "MAPK14" "CDKN2A" "CDKN1A" "CCNE1" "CCNA1" "MAPKAPK5" "CBX4" "TXN" "TBX2"
[12] "STAT3" "SRF" "BMI1" "MAP2K4" "MAP2K6" "MAP2K3" "MAPK8" "MAPK3" "MAPK1" "PRKCD" "PML"
[23] "OPA1" "ATM" "MDM2" "CXCL8" "IL6" "IGFBP7" "ID1" "HRAS" "H2AFX" "POT1" "SIRT1"
[34] "KDM6B" "PLA2R1" "EZH2" "E2F3" "E2F1" "CEBPB" "CDKN2D" "CDKN2B" "CDKN1B" "CDK6" "CDK4"
[45] "CDK2" "CDC42" "RBX1" "CDC27" "CDK1" "MAML1" "CD44" "MAD2L1BP" "MAP4K4" "AIM2" "RECQL4"
[56] "ARHGAP18" "KL" "MAPKAPK2" "AURKB" "SLC16A7" "CCNE2" "HIST1H2BJ" "HIST1H3F" "CCNA2" "MCM3AP" "CDC16"
[67] "TSC22D1" "CBS" "TNFSF13" "CTNNAL1" "EED" "PNPT1" "CDC23" "RNASET2" "TP63" "CAV1" "MKNK1"
[78] "TSLP" "HIST1H2BK" "PPM1D" "HAVCR2" "CBX2" "KDM2B" "DPY30" "C2orf40" "YPEL3" "HIST2H4A" "HIST1H4L"
[89] "HIST1H4E" "HIST1H4B" "HIST1H4H" "HIST1H4C" "HIST1H4J" "HIST1H4K" "HIST1H4F" "HIST1H4D" "HIST1H4A" "HIST1H3B" "HIST1H3H"
[100] "HIST1H3J" "HIST1H3G" "HIST1H3I" "HIST1H3E" "HIST1H3C" "HIST1H3D" "HIST1H3A" "HIST2H2BE" "HIST1H2BO" "HIST1H2BC" "HIST1H2BI"
[111] "HIST1H2BH" "HIST1H2BE" "HIST1H2BF" "HIST1H2BM" "HIST1H2BN" "HIST1H2BL" "HIST1H2BG" "HIST2H2AC" "HIST2H2AA3" "HIST1H2AB" "HIST1H2AC"
[122] "HIST1H2AJ" "HIST1H4I" "HIST3H3" "CALR" "HMGA2" "PHC3" "KAT6A" "EHMT1" "SMC6" "AIMP2" "CALCA"
[133] "DEK" "MAPKAPK3" "ZNF148" "YY1" "WRN" "WNT5A" "NR1H2" "UBE3A" "UBE2E1" "UBE2D1" "UBC"
[144] "UBB" "UBA52" "CDC26P1" "TYMS" "TWIST1" "HIRA" "RPS27AP11" "HIST2H2AA4" "TP73" "TOPÂ 1,00" "TNF"
[155] "TGFB2" "TGFB1" "TFDP1" "TERT" "TERF1" "BUB1B" "BUB1" "TCF3" "TBX3" "TAGLN" "STAT6"
[166] "STAT1" "BRAF" "SREBF1" "BRCA1" "SP1" "SOX5" "SOD2" "SNAI1" "SMARCB1" "SMARCA2" "HIST2H3D"
[177] "PHC1P1" "ACD" "SKIL" "LOC649620" "SLC13A3" "LOC647654" "SMURF2" "ANAPC1" "SHC1" "CPEB1" "H3F3AP6"
[188] "ZMAT3" "RBBP4P1" "SRSF3" "SRSF1" "SATB1" "S100A6" "RXRB" "RRM2" "RRM1" "RPS27A" "RPS6KA3"
[199] "RPS6KA2" "RPS6KA1" "RPL5" "RNF2" "RIT1" "RING1" "BCL2L1" "RELA" "BCL2" "CCND1" "RBP2"
[210] "RBL2" "RBL1" "RBBP7" "RBBP4" "NTN4" "RB1" "IL21" "RAN" "RAF1" "RAC1" "TNRC6C"
[221] "KIAA1524" "EP400" "CNOT6" "CBX8" "PTEN" "SEPN1" "BACH1" "PSMB5" "PROX1" "PRL" "MAP2K7"
[232] "MAP2K1" "MAPK10" "MAPK9" "MAPK11" "MAPK7" "PRKDC" "RNF114" "PRKCI" "ATF7IP" "MFN1" "PRKAA2"
[243] "CDKN2AIP" "RBM38" "PRG2" "HIST2H4B" "HJURP" "TMEM140" "PBRM1" "Mar-05" "PPARG" "PPARD" "POU2F1"
[254] "TERF2IP" "ERRFI1" "H2BFS" "PLK1" "PLAUR" "PIN1" "PIM1" "PIK3CA" "PHB" "PGR" "PGD"
[265] "PIAS4" "PDGFB" "SIRT6" "ANAPC11" "ANAPC7" "ANAPC5" "WNT16" "FZR1" "ZBTB7A" "ERGIC2" "PCNA"
[276] "FIS1" "PAX3" "NOX4" "MINK1" "PEBP1" "YBX1" "NINJ1" "NFKB1" "H2AFB1" "NDN" "NCAM1"
[287] "NBN" "MYC" "MYBL2" "MSN" "ASS1" "LOC441488" "MRE11A" "MOV10" "MMP7" "MIF" "MAP3K5"
[298] "MAP3K1" "MECP2" "MCL1" "MAGEA2" "SMAD9" "SMAD7" "SMAD6" "SMAD5" "SMAD4" "SMAD3" "SMAD2"
[309] "SMAD1" "MAD2L1" "MXD1" "MIR34A" "MIR30A" "MIR299" "MIR29A" "MIR22" "MIR217" "MIR21" "MIR205"
[320] "MIR203A" "MIR191" "MIR146A" "MIR141" "MIR10B" "ARNTL" "LMNB1" "LMNA" "LGALS9" "RHOA" "KRT5"
[331] "KRAS" "KIT" "KIR2DL4" "KCNJ12" "JUN" "JAK2" "ITGB4" "IRS1" "IRF7" "IRF5" "IRF3"
[342] "ING1" "IDO1" "ILF3" "IL15" "IL12B" "CXCR2" "IL4" "IGFBP5" "IGFBP3" "IGFBP1" "IGF1R"
[353] "IGF1" "H3F3AP5" "IFNG" "IFI16" "IDH1" "ID2" "HIST2H3A" "BIRC5" "HSPB1" "HSPA9" "HSPA5"
[364] "HSPA1A" "APEX1" "HNRNPA1" "FOXA3" "FOXA2" "FOXA1" "HMGA1" "HIF1A" "ANXA5" "HELLS" "HDAC1"
[375] "H3F3B" "H3F3A" "HIST1H2BB" "HIST1H2BD" "H2AFZ" "HIST1H2AD" "HIST1H2AE" "ANAPC4" "ANAPC2" "UBN1" "SENP1"
[386] "GUCY2C" "GSK3B" "UHRF1" "BRD7" "NSMCE2" "PTRF" "GPI" "GNAO1" "RPS6KA6" "TNRC6A" "AGO2"
[397] "B3GAT1" "DNAJC2" "GJA1" "AGO1" "EHF" "TINF2" "LDLRAP1" "ULK3" "GAPDH" "ABI3BP" "ASF1A"
[408] "HIST1H2BA" "G6PD" "ACKR1" "MTOR" "CDC26" "CNOT6L" "FOS" "CABIN1" "MORC3" "SUZ12" "NPTXR"
[419] "CBX6" "SIRT3" "CRTC1" "PPP1R13B" "SUN1" "SMC5" "TNRC6B" "FOXO1" "FOXM1" "TNIK" "SCMH1"
[430] "DKKÂ 1,00" "FGFR2" "FGF2" "HEPACAM" "FANCD2" "EWSR1" "ETS2" "ETS1" "ESR2" "ERF" "AKT1"
[441] "EREG" "ERBB2" "ENG" "ELN" "CRTC2" "EIF5A" "EGR1" "EGFR" "EEF1B2" "AGO4" "AGO3"
[452] "EEF1A1" "PHC2" "PHC1" "ABCA1" "E2F2" "DUSP6" "DUSP4" "HBEGF" "AGT" "DNMT3A" "AGER"
[463] "DKC1" "DAXX" "CYP3A4" "CTSZ" "CTSD" "CSNK2A1" "E2F7" "PARP1" "HIST3H2BB" "HIST2H3C" "JDP2"
[474] "HIST4H4" "CLU" "CKB" "RASSF1" "CHEK1" "TOPBP1" "UBE2C" "KIF2C" "BTG3" "EHMT2" "GADD45G"
[485] "NEK6" "ZMYND11" "SPINT2" "CENPA" "AGR2" "CEBPG" "HYOU1" "TADA3" "MCRS1" "NDRG1" "ANAPC10"
[496] "CDKN2C" "ZMPSTE24" "PSMD14" "NAMPT" "RAD50" "TRIM10" "DNM1L" "BCL2L11"
$GOBP_CELLULAR_SENESCENCE
[1] "AKT3" "MIR543" "CDK2" "CDK6" "CDKN1A" "ZMPSTE24" "CDKN1B" "CDKN2A" "CDKN2B" "CITED2" "KAT5" "PLK2" "NEK6" "ZNF277"
[15] "CGAS" "COMP" "MAPK14" "VASH1" "PLA2R1" "SMC5" "SIRT1" "MORC3" "NUP62" "ABL1" "ULK3" "RSL1D1" "FBXO5" "FBXO4"
[29] "MAGEA2B" "NSMCE2" "H2AX" "HLA-G" "HMGA1" "HRAS" "ID2" "IGF1R" "ING2" "KIR2DL4" "ARG2" "LMNA" "BMAL1" "MIR10A"
[43] "MIR146A" "MIR17" "MIR188" "MIR217" "MIR22" "MIR34A" "MAGEA2" "MAP3K3" "MAP3K5" "MIF" "MNT" "ATM" "NPM1" "YBX1"
[57] "OPA1" "PAWR" "ABI3" "FZR1" "WNT16" "SIRT6" "PML" "PRMT6" "PRELP" "PRKCD" "MAPK8" "MAPK11" "MAPK9" "MAPK10"
[71] "MAP2K1" "MAP2K3" "MAP2K6" "MAP2K7" "B2M" "ZMIZ1" "PTEN" "MIR20B" "RBL1" "BCL6" "MAP2K4" "BMPR1A" "SPI1" "SRF"
[85] "BRCA2" "NEK4" "TBX2" "TBX3" "MIR590" "TERC" "TERF2" "TERT" "TOP2B" "TP53" "TWIST1" "WNT1" "WRN" "SMC6"
[99] "KAT6A" "ZKSCAN3" "HMGA2" "CALR" "YPEL3" "ECRG4" "MAPKAPK5" "TP63" "PNPT1" "DNAJA3" "EEF1E1" "NUAK1"
$GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
$GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
$HernandezSegura
$REACTOME_CELLULAR_SENESCENCE
[1] "CDC27" "E2F2" "SCMH1" "MRE11" "MAP2K3" "MAPK9" "ANAPC4" "MAP2K4" "MAP4K4" "RPS6KA2" "UBE2D1" "EED" "MAP2K7" "TNRC6C"
[15] "MAPKAPK5" "ANAPC5" "TNRC6A" "TINF2" "AGO1" "CDC23" "CABIN1" "MAPK1" "HIRA" "TNRC6B" "E2F1" "RBBP7" "MAPK3" "ACD"
[29] "NBN" "CCNE1" "FZR1" "ERF" "CDK6" "H2AZ2" "EZH2" "MAPK8" "UBE2S" "MAP2K6" "NFKB1" "MAPK10" "ANAPC15" "CDKN1B"
[43] "PHC1" "ASF1A" "MAPK14" "E2F3" "LMNB1" "RAD50" "TFDP2" "MAPKAPK3" "IL1A" "RPS6KA1" "UBN1" "RNF2" "CDKN2C" "CDK2"
[57] "H1-3" "H1-1" "H2BC11" "CDKN1A" "ID1" "AGO3" "POT1" "CDKN2D" "CDC16" "H3-3B" "KDM6B" "TERF2" "CCNA1" "PHC2"
[71] "AGO4" "ETS1" "CDK4" "MDM2" "IL6" "TXN" "HMGA1" "RB1" "MINK1" "TP53" "ANAPC11" "CBX8" "CBX4" "RPS27A"
[85] "CCNA2" "H2BC1" "TERF1" "CDKN2B" "CDKN2A" "ATM" "HMGA2" "UBC" "VENTX" "ANAPC1" "TNIK" "MOV10" "ETS2" "H2BC5"
[99] "H4C8" "RBBP4" "MAPKAPK2" "H3-3A" "IGFBP7" "ANAPC10" "ANAPC16" "MAPK7" "TERF2IP" "H3-4" "BMI1" "H1-4" "STAT3" "CXCL8"
[113] "UBE2E1" "UBB" "FOS" "IFNB1" "CEBPB" "KAT5" "RELA" "PHC3" "CBX2" "UBE2C" "CCNE2" "ANAPC2" "CDC26" "RPS6KA3"
[127] "JUN" "SUZ12" "H2AC6" "H2BC4" "EHMT1" "EP400" "H3C13" "CBX6" "H2AC20" "H1-5" "H2BC21" "H2BC13" "MAPK11" "SP1"
[141] "H1-2" "H2AX" "H1-0" "ANAPC7" "H2AC7" "H2BC26" "H4C3" "H3C12" "H4C11" "H3C4" "MAP3K5" "H4C16" "H2BC12" "TFDP1"
[155] "MDM4" "H3C14" "H3C15" "RING1" "EHMT2" "UBA52" "H2AJ" "H4C15" "H4C14" "H4C12" "H2BC14" "H2BC8" "H3C8" "H2AB1"
[169] "H2BC6" "H4C6" "H2BC17" "H3C6" "H4C13" "H3C11" "H2BC9" "H3C1" "H4C9" "H2AC14" "H2BC3" "H4C5" "H2AC8" "H4C4"
[183] "H2BC7" "H3C7" "H2AC4" "H2BC10" "H4C1" "H4C2" "H3C10" "MIR24-2" "MIR24-1" "H3C2" "H3C3" "H2AC18" "H2AC19"
$SAUL_SEN_MAYO
$SeneQuestNAdf_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = bidirectsigs)Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuestsenescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition", method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ssgsea <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = bidirectsigs)Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variablessenescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ssgsea, ColorVariable = "CellType", GroupingVariable="Condition", method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = bidirectsigs)Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuestsenescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition", method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
Heatmap for Cohen’s D
PlotScores(data = corrcounts_merge,
metadata = metadata_merge,
gene_sets=bidirectsigs,
GroupingVariable="Condition",
method ="all",
ncol = NULL,
nrow = NULL,
widthTitle=30,
limits = NULL,
title="Marthandan et al. 2016",
titlesize = 12,
ColorValues = NULL) Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
# missing:
# - combine legends
# - wrap title
# - tilt x labels to 60 degrees
# - change default colors
# wrap x labels with wrap_title
# grid with common legends https://support.bioconductor.org/p/87318/
Correlation plots
If the user is investigating if a certain variable can be described from the score, and not already knowing that variable. More exploratory…
- Define what each variable is: Numeric (includes integer if unique > 5), Categorical Bin (integer/string if unique == 2, logical), Categorical Multi (integer if unique < 5, string if unique > 2)
- Define functions to calculate metrics based on different data types:
- Categorical Bin: t-test/wilcoxon
- Categorical Multi: ANOVA / Kruskal-Wallis + Tukey’s test
- Numeric : Pearson / Spearman / Kendall’s Tau
- Return a list:
- One entry per variable
- Method used
- Data frame
- Two columns: metric and p-value
- One line per subvariable (if Numeric and Categorical Bin, only one line; for Categorical Multi, one per combination of variables); named rows
- One entry per variable
- Plot results
- If Numeric, scatter plot; metric on the top left corner
- If Categorical, density plot, colored by the unique values of the variable; metrics (if one, or combinations of variables) in top left corner
- Arrange in grid
options(error=recover)
plot_stat_tests(metadata_corr, target_var="score", cols = c("Condition","Is_Senescent","random_cat","random_numeric"),
discrete_colors = list(Is_Senescent=c("Senescent"="pink",
"Non Senescent"="orange")),
continuous_color = "#8C6D03",
color_palette = "Set2", nrow=1, sizeannot=3, legend.position="top")`geom_smooth()` using formula = 'y ~ x'
Individual Genes
Violin Expression Plots
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
IndividualGenes_Violins(data = corrcounts_merge, metadata = metadata_merge, genes = c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"), GroupingVariable = "Condition", plot=T, ncol=NULL, nrow=2, divide="CellType", invert_divide=FALSE,ColorValues=senescence_triggers_colors, pointSize=2, ColorVariable="SenescentType", title="Senescence", widthTitle=16,y_limits = NULL,legend_nrow=4, xlab="Condition",colorlab="") Correlation Heatmap
options(error=recover)
CorrelationHeatmap(data=corrcounts_merge,
metadata = metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
separate.by = "Condition",
method = "pearson",
colorlist = list(low = "#3F4193", mid = "#F9F4AE", high = "#B44141"),
limits_colorscale = c(-1,0,1),
widthTitle = 16,
title = "test",
cluster_rows = TRUE,
cluster_columns = TRUE,
detailedresults = FALSE,
legend_position="right",
titlesize=20)
Expression Heatmaps
options(error=recover)
annotation_colors <- list(
CellType = c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
),
Condition = c(
"Senescent" = "#65AC7C", # Example color: greenish
"Proliferative" = "#5F90D4", # Example color: blueish
"Quiescent" = "#EDA03E" # Example color: orange
)
)
ExpressionHeatmap(data=corrcounts_merge,
metadata = metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
annotate.by = c("CellType","Condition"),
annotation_colors = annotation_colors,
colorlist = list(low = "#3F4193", mid = "#F9F4AE", high = "#B44141"),
cluster_rows = TRUE,
cluster_columns = FALSE,
title = "test",
titlesize = 20,
legend_position = "right",
scale_position="right")
ROC/AUC
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
ROCandAUCplot(corrcounts_merge,
metadata_merge,
condition_var = "Condition",
class = "Senescent",
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
group_var="CellType",
plot_type = "all",
heatmap_params = list(col = list( "#F9F4AE" ,"#B44141"),
limits = c(0.5,1),
cluster_rows=T),
roc_params = list(nrow=2,
ncol=2,
colors=cellTypes_colors),
commomplot_params = list(widths=c(0.5,0.5)))
Cohen’s d
CohenDHeatmap(corrcounts_merge,
metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
condition_var = "Condition",
class = "Senescent",
group_var = "CellType",
title = NULL,
widthTitle = 16,
heatmap_params = list(col = list( "#F9F4AE" ,"#B44141"),
limits = NULL,
cluster_rows=T))PCA with genes from signature only
CellTypecols = c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
sencols <- c(
"Senescent" = "#D32F2F", # Strong salmon
"Quiescent" = "#FFB74D", # Bright peach
"Proliferative" = "#9575CD" # Deep pastel purple
)
plotPCA(data=corrcounts_merge,
metadata=metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
scale=FALSE,
center=TRUE,
PCs=list(c(1,2), c(2,3), c(3,4)),
ColorVariable="Condition",
ColorValues=sencols,
pointSize=5,
legend_nrow=1,
ncol=3,
nrow=NULL)Enrichment-based
GSEA
options(error=recover)
degenes <- calculateDE(data=corrcounts_merge,
metadata=metadata_merge,
variables="Condition",
lmexpression = NULL,
modelmat = NULL,
contrasts = c("Senescent - Proliferative",
"Senescent - Quiescent",
"Proliferative - Quiescent"))
degenes$`Senescent - Proliferative`
$`Senescent - Quiescent`
$`Proliferative - Quiescent`NAoptions(error=recover)
plotVolcano(DEResultsList=degenes, genes=bidirectsigs, N=NULL, x="logFC",y="-log10(adj.P.Val)", pointSize=2, color="pink", highlightcolor="darkblue", highlightcolor_upreg = "#038C65", highlightcolor_downreg = "#8C0303", nointerestcolor="grey",threshold_y=NULL, threshold_x=NULL, xlab=NULL, ylab=NULL, ncol=NULL, nrow=NULL, title=NULL,labsize=7,widthlabs=25, invert=T)
GSEAresults <- runGSEA(degenes, bidirectsigs, stat = NULL)
GSEAresultsplotGSEAenrichment(GSEA_results=GSEAresults, DEGList=degenes, gene_sets=bidirectsigs, widthTitle=32,grid = T, titlesize = 10, nrow=3, ncol=9)
plotNESlollipop(GSEA_results=GSEAresults, sig_threshold = 0.05,
low_color = "blue", mid_color = "white", high_color = "red",
grid = T, nrow = 1, ncol = NULL, padj_limit=c(0,0.1), widthlabels=28, title=NULL)
plotCombinedGSEA(GSEAresults, sig_threshold = 0.05, PointSize=9, widthlegend = 26 )Association with Phenotype
If the user is investigating if a certain variable can be described from the GSEA results, and not already knowing that variable. More exploratory…
For each variable, define all possible contrasts (e.g. if A,B,C, then consider A-B, A-C, B-C,A-(B+C)/2, etc…)
Use calculateDE without baseline and all possible contrasts
perform GSEA and collect all results in only one table
Plot all results in lollipop plots, y axis with all contrasts
One function to calculate all results; one to do the lollypop plot
# Example usage
levels <- c("A", "B" ,"C","D")
generate_all_contrasts(levels, mode="extensive") [1] "A - B" "B - A" "A - C" "C - A" "A - D" "D - A"
[7] "B - C" "C - B" "B - D" "D - B" "C - D" "D - C"
[13] "A - ( B + C + D )/ 3" "( B + C + D )/ 3 - A" "B - ( A + C + D )/ 3" "( A + C + D )/ 3 - B" "C - ( A + B + D )/ 3" "( A + B + D )/ 3 - C"
[19] "D - ( A + B + C )/ 3" "( A + B + C )/ 3 - D" "(A + B)/2 - (C + D)/2" "(C + D)/2 - (A + B)/2" "(A + C)/2 - (B + D)/2" "(B + D)/2 - (A + C)/2"
[25] "(A + D)/2 - (B + C)/2" "(B + C)/2 - (A + D)/2" options(error=recover)
df_test <- GSEA_VariableAssociation(data=corrcounts_merge,
metadata=metadata_merge_corr,
cols=c("Condition","Is_Senescent","random_cat","random_numeric"),
mode="extensive",
gene_set=list(HernandezSegura=bidirectsigs$HernandezSegura),
padj_limit = c(0, 0.1), low_color = "blue", mid_color = "white", high_color = "red",
sig_threshold = 0.05, widthlabels=30, labsize=10, titlesize=14) Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
There were 1 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.df_test$plotWarning: Removed 1 row containing missing values or values outside the scale range (`geom_segment()`).
Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).
metadata_corr_1 <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = list(HernandezSegura=bidirectsigs$HernandezSegura) )Considering bidirectional gene signature mode for signature HernandezSegura
No id variables; using all as measure variables
No id variables; using all as measure variablesmetadata_corr <- metadata_corr_1$`HernandezSegura`
metadata_corr <- merge(metadata_corr,metadata_merge_corr[,c("sampleID","random_cat","random_numeric","Is_Senescent")], by.x="sample", by.y="sampleID",all.x = T)
metadata_corr
plot_stat_tests(metadata_corr, target_var="score", cols = c("Condition","Is_Senescent","random_cat","random_numeric"),
discrete_colors = list(Is_Senescent=c("Senescent"="pink",
"Non Senescent"="orange")),
continuous_color = "#8C6D03",
color_palette = "Set2", nrow=4, sizeannot=3, legend.position="top")`geom_smooth()` using formula = 'y ~ x'
degenes_test <- calculateDE(data=corrcounts_merge,
metadata=metadata_merge_corr,
variables="random_cat",
lmexpression = NULL,
modelmat = NULL,
contrasts = generate_all_contrasts(c(unique(metadata_corr$random_cat)), mode = "extensive"))
GSEAresults_test <- runGSEA(degenes_test, list(HernandezSegura_UP=SenescenceSignatures$`[UP]_HernandezSegura`), stat = NULL)Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.GSEAresults_test$`C-B`
$`B-C`
$`C-A`
$`A-C`
$`B-A`
$`A-B`
$`C-(B+A)/2`
$`(B+A)/2-C`
$`B-(C+A)/2`
$`(C+A)/2-B`
$`A-(C+B)/2`
$`(C+B)/2-A`
options(error=recover)
plotGSEAenrichment(GSEA_results=GSEAresults_test, DEGList=degenes_test, gene_sets=list(HernandezSegura_UP=SenescenceSignatures$`[UP]_HernandezSegura`), widthTitle=32,grid = F) $`C-B_HernandezSegura_UP`
$`B-C_HernandezSegura_UP`
$`C-A_HernandezSegura_UP`
$`A-C_HernandezSegura_UP`
$`B-A_HernandezSegura_UP`
$`A-B_HernandezSegura_UP`
$`C-(B+A)/2_HernandezSegura_UP`
$`(B+A)/2-C_HernandezSegura_UP`
$`B-(C+A)/2_HernandezSegura_UP`
$`(C+A)/2-B_HernandezSegura_UP`
$`A-(C+B)/2_HernandezSegura_UP`
$`(C+B)/2-A_HernandezSegura_UP`
NA options(error=recover)
df_test <- GSEA_VariableAssociation(data=corrcounts_merge,
metadata=metadata_merge_corr,
cols=c("Condition" ),
mode="medium",
gene_set=list(HernandezSegura_UP=SenescenceSignatures$`[UP]_HernandezSegura` ),
padj_limit = c(0, 0.1), low_color = "blue", mid_color = "white", high_color = "red",
sig_threshold = 0.05, widthlabels=30, labsize=10, titlesize=14) Error in .subset2(x, i, exact = exact) : subscript out of bounds
Enter a frame number, or 0 to exit
1: GSEA_VariableAssociation(data = corrcounts_merge, metadata = metadata_merge_corr, cols = c("Condition"), mode = "medium", gene_set = list(HernandezS
2: #40: runGSEA(DEGs_var, gene_sets = gene_set, stat = stat)
3: runGSEA.R#65: setNames(deg_df[[current_stat]], rownames(deg_df))
4: deg_df[[current_stat]]
5: `[[.data.frame`(deg_df, current_stat)
6: (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, i, exact = exact))(x, ..., exact = exact)1Called from: setNames(deg_df[[current_stat]], rownames(deg_df))c
Enter a frame number, or 0 to exit
1: GSEA_VariableAssociation(data = corrcounts_merge, metadata = metadata_merge_corr, cols = c("Condition"), mode = "medium", gene_set = list(HernandezS
2: #40: runGSEA(DEGs_var, gene_sets = gene_set, stat = stat)
3: runGSEA.R#65: setNames(deg_df[[current_stat]], rownames(deg_df))
4: deg_df[[current_stat]]
5: `[[.data.frame`(deg_df, current_stat)
6: (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, i, exact = exact))(x, ..., exact = exact)2Called from: deg_df[[current_stat]]View(deg_df)
c
Enter a frame number, or 0 to exit
1: GSEA_VariableAssociation(data = corrcounts_merge, metadata = metadata_merge_corr, cols = c("Condition"), mode = "medium", gene_set = list(HernandezS
2: #40: runGSEA(DEGs_var, gene_sets = gene_set, stat = stat)
3: runGSEA.R#65: setNames(deg_df[[current_stat]], rownames(deg_df))
4: deg_df[[current_stat]]
5: `[[.data.frame`(deg_df, current_stat)
6: (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, i, exact = exact))(x, ..., exact = exact)2Called from: deg_df[[current_stat]]setNames(deg_df[[current_stat]], rownames(deg_df))Error during wrapup: subscript out of bounds
Error: no more error handlers available (recursive errors?); invoking 'abort' restartcurrent_stat[1] "t" "B"is.data.frame(gs)[1] FALSEQ
df_test$plot
df_test <- GSEA_VariableAssociation(data=corrcounts_merge,
metadata=metadata_merge_corr,
cols=c("Condition" ),
mode="medium",
gene_set=list( HernandezSegura_UP_Bidirect=subset(bidirectsigs$HernandezSegura, enrichment==1)),
padj_limit = c(0, 0.1), low_color = "blue", mid_color = "white", high_color = "red",
sig_threshold = 0.05, widthlabels=30, labsize=10, titlesize=14) Error in .subset2(x, i, exact = exact) : subscript out of bounds
Enter a frame number, or 0 to exit
1: GSEA_VariableAssociation(data = corrcounts_merge, metadata = metadata_merge_corr, cols = c("Condition"), mode = "medium", gene_set = list(HernandezS
2: #40: runGSEA(DEGs_var, gene_sets = gene_set, stat = stat)
3: runGSEA.R#65: setNames(deg_df[[current_stat]], rownames(deg_df))
4: deg_df[[current_stat]]
5: `[[.data.frame`(deg_df, current_stat)
6: (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, i, exact = exact))(x, ..., exact = exact)#' GSEA Variable AssociationEnter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' This function assesses the association between gene expression (or another molecular score)Enter an item from the menu, or 0 to exit#' and metadata variables using differential expression (DE) analysis and Gene Set Enrichment Analysis (GSEA).Enter an item from the menu, or 0 to exit#' It generates all possible contrasts for categorical variables and uses linear modeling for continuous variables.Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' @param data A matrix or data frame containing gene expression data, where rows represent genes and columns represent samples.Enter an item from the menu, or 0 to exit#' @param metadata A data frame containing sample metadata with at least one column corresponding to the variables of interest.Enter an item from the menu, or 0 to exit#' @param cols A character vector specifying the metadata columns (variables) to analyze.Enter an item from the menu, or 0 to exit#' @param stat Optional. The statistic to use for ranking genes before GSEA. If `NULL`, it is automatically determined based on the gene set:Enter an item from the menu, or 0 to exit#' - `"B"` for gene sets with **no known direction** (vectors).Enter an item from the menu, or 0 to exit#' - `"t"` for **unidirectional** or **bidirectional** gene sets (data frames).Enter an item from the menu, or 0 to exit#' - If provided, this argument overrides the automatic selection.Enter an item from the menu, or 0 to exit#' @param mode A character string specifying the contrast generation method for categorical variables. Options: `"simple"`, `"medium"`, `"extensive"`. Default is `"simple"`.Enter an item from the menu, or 0 to exit#' @param gene_set A named list defining the gene sets for GSEA. **(Required)**Enter an item from the menu, or 0 to exit#' - If using **unidirectional** gene sets, provide a list where each element is a vector of gene names representing a signature.Enter an item from the menu, or 0 to exit#' - If using **bidirectional** gene sets, provide a list where each element is a data frame:Enter an item from the menu, or 0 to exit#' - The **first column** should contain gene names.Enter an item from the menu, or 0 to exit#' - The **second column** should indicate the expected direction of enrichment (`1` for upregulated, `-1` for downregulated).Enter an item from the menu, or 0 to exit#' @param padj_limit A numeric vector of length 2 specifying the color scale limits for adjusted p-values in the plot. Default: `c(0, 0.1)`.Enter an item from the menu, or 0 to exit#' @param low_color The color representing low adjusted p-values in the plot. Default: `"blue"`.Enter an item from the menu, or 0 to exit#' @param mid_color The color representing the chosen value for `sig_threshold`. Default: `"white"`.Enter an item from the menu, or 0 to exit#' @param high_color The color representing high adjusted p-values in the plot. Default: `"red"`.Enter an item from the menu, or 0 to exit#' @param sig_threshold A numeric value specifying the threshold for significance visualization in the plot. Default: `0.05`.Enter an item from the menu, or 0 to exit#' @param widthlabels An integer controlling the maximum width of contrast labels before text wrapping. Default: `18`.Enter an item from the menu, or 0 to exit#' @param labsize An integer controlling the axis text size in the plot. Default: `10`.Enter an item from the menu, or 0 to exit#' @param titlesize An integer specifying the plot title size. Default: `14`.Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' @return A list with two elements:Enter an item from the menu, or 0 to exit#' - `data`: A data frame containing the GSEA results, including normalized enrichment scores (NES), adjusted p-values, and contrasts.Enter an item from the menu, or 0 to exit#' - `plot`: A ggplot2 object visualizing the GSEA results as a lollipop plot.Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' @examplesEnter an item from the menu, or 0 to exit#' # Example usage with random dataEnter an item from the menu, or 0 to exit#' data <- matrix(rnorm(1000), ncol = 10)Enter an item from the menu, or 0 to exit#' metadata <- data.frame(group = rep(c("A", "B"), each = 5))Enter an item from the menu, or 0 to exit#' gene_set <- list(SampleSet = c("Gene1", "Gene2", "Gene3"))Enter an item from the menu, or 0 to exit#' results <- GSEA_VariableAssociation(data, metadata, cols = "group", gene_set = gene_set)Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' # View resultsEnter an item from the menu, or 0 to exit#' print(results$data)Enter an item from the menu, or 0 to exit#' print(results$plot)Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' @exportEnter an item from the menu, or 0 to exitGSEA_VariableAssociation <- function(data, metadata, cols, stat=NULL, mode=c("simple","medium","extensive"), gene_set, padj_limit = c(0, 0.1), low_color = "blue", mid_color = "white", high_color = "red", sig_threshold = 0.05, widthlabels=18, labsize=10, titlesize=14) {Enter an item from the menu, or 0 to exit mode <- match.arg(mode)Enter an item from the menu, or 0 to exit metadata <- metadata[, cols %in% colnames(metadata), drop = FALSE]Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Identify variable typesEnter an item from the menu, or 0 to exit variable_types <- identify_variable_type(metadata, cols)Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Store results and plotsEnter an item from the menu, or 0 to exit all_results <- list() # Store GSEA resultsEnter an item from the menu, or 0 to exit all_plots <- list() # Store plotsEnter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit cont_vec <- c()Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit for (var in cols) {Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Check if the variable exists in metadataEnter an item from the menu, or 0 to exit if (!(var %in% colnames(metadata))) {Enter an item from the menu, or 0 to exit warning(paste0("Variable ", var, " not in metadata"))Enter an item from the menu, or 0 to exit next # Skip to the next iteration if the variable is not in metadataEnter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Handle based on variable typeEnter an item from the menu, or 0 to exit if (variable_types[var] == "Numeric") {Enter an item from the menu, or 0 to exit # Use a linear model expression for continuous variablesEnter an item from the menu, or 0 to exit DEGs_var <- calculateDE(data = data, metadata = metadata, lmexpression = paste0("~", var))Enter an item from the menu, or 0 to exit cont_vec <- c(cont_vec,c(paste0("intercept_",var),var))Enter an item from the menu, or 0 to exit } else {Enter an item from the menu, or 0 to exit # For categorical variables, generate contrastsEnter an item from the menu, or 0 to exit uniquevalues_var <- unique(metadata[, var])Enter an item from the menu, or 0 to exit uniquevalues_var <- gsub(" ", "", uniquevalues_var)Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit contrasts <- generate_all_contrasts(uniquevalues_var, mode = mode)Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Calculate differential expression results for each contrastEnter an item from the menu, or 0 to exit DEGs_var <- calculateDE(data = data, metadata = metadata, variables = var, contrasts = contrasts)Enter an item from the menu, or 0 to exit cont_vec <- c(cont_vec,contrasts)Enter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit set.seed("20032025")Enter an item from the menu, or 0 to exit # Perform GSEA for each caseEnter an item from the menu, or 0 to exit GSEA_results <- runGSEA(DEGs_var, gene_sets = gene_set, stat=stat)Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Collect GSEA results in a listEnter an item from the menu, or 0 to exit all_results[[var]] <- GSEA_resultsEnter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Combine results into a data frameEnter an item from the menu, or 0 to exit combined_results <- do.call(rbind, lapply(all_results, function(x) do.call(rbind, x)))Enter an item from the menu, or 0 to exit combined_results$Contrast <- cont_vecEnter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # correct adjusted p value to correct for multiple testing for the contrasts?Enter an item from the menu, or 0 to exit #combined_results$padj <- p.adjust(combined_results$padj, method = "BH")Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit combined_results_toreturn <- combined_resultsEnter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit plot_list <- list()Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Ensure contrast orderingEnter an item from the menu, or 0 to exit combined_results$Contrast <- sapply(combined_results$Contrast, function(x) wrap_title(x, widthlabels))Enter an item from the menu, or 0 to exit combined_results$Contrast <- factor(combined_results$Contrast, levels = combined_results$Contrast[order(combined_results$NES)])Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit plot <- ggplot2::ggplot(combined_results, ggplot2::aes(x = NES, y = Contrast,fill = padj)) +Enter an item from the menu, or 0 to exit ggplot2::geom_segment(ggplot2::aes(yend = Contrast, xend = 0), size = .5) +Enter an item from the menu, or 0 to exit ggplot2::geom_point( shape = 21, stroke = 1.2, color="black", size=4) +Enter an item from the menu, or 0 to exit ggplot2::scale_fill_gradient2(low = low_color, mid = mid_color, high = high_color, midpoint = sig_threshold, limits = padj_limit, na.value=high_color) +Enter an item from the menu, or 0 to exit ggplot2::labs(title = unique(combined_results$pathway), x = "Normalized Enrichment Score (NES)", y = "Contrast", color = "Adj. p-value", fill = "Adj. p-value") +Enter an item from the menu, or 0 to exit ggplot2::theme_minimal() +Enter an item from the menu, or 0 to exit ggplot2::theme(Enter an item from the menu, or 0 to exit plot.title = ggplot2::element_text(hjust = 0.5, face = "bold", size=titlesize),Enter an item from the menu, or 0 to exit legend.position = "right",Enter an item from the menu, or 0 to exit axis.text = ggplot2::element_text(size=labsize)Enter an item from the menu, or 0 to exit )Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit return(list(plot=plot,Enter an item from the menu, or 0 to exit data=combined_results_toreturn)) # Return list if not in grid modeEnter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit}Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit#' Generate All Possible Contrasts Between GroupsEnter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' This function creates statistical contrasts between levels of a categorical variable.Enter an item from the menu, or 0 to exit#' Users can choose the level of complexity:Enter an item from the menu, or 0 to exit#' - `"simple"`: Pairwise comparisons (e.g., A - B).Enter an item from the menu, or 0 to exit#' - `"medium"`: Pairwise comparisons plus comparisons against the mean of other groups.Enter an item from the menu, or 0 to exit#' - `"extensive"`: All possible groupwise contrasts, ensuring balance in the number of terms on each side.Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' @param levels A character vector of unique group levels.Enter an item from the menu, or 0 to exit#' @param mode A string specifying the level of detail for contrasts.Enter an item from the menu, or 0 to exit#' Options are `"simple"` (pairwise only), `"medium"` (pairwise + vs. mean of others), or `"extensive"` (all possible balanced groupwise contrasts). Default is `"extensive"`.Enter an item from the menu, or 0 to exit#'Enter an item from the menu, or 0 to exit#' @return A character vector of contrast expressions (without names).Enter an item from the menu, or 0 to exit#' @examplesEnter an item from the menu, or 0 to exit#' levels <- c("A", "B", "C", "D")Enter an item from the menu, or 0 to exit#' generate_all_contrasts(levels, mode = "simple") # Pairwise onlyEnter an item from the menu, or 0 to exit#' generate_all_contrasts(levels, mode = "medium") # Pairwise + mean comparisonsEnter an item from the menu, or 0 to exit#' generate_all_contrasts(levels, mode = "extensive") # All balanced contrastsEnter an item from the menu, or 0 to exit#' @exportEnter an item from the menu, or 0 to exitgenerate_all_contrasts <- function(levels, mode = "extensive") {Enter an item from the menu, or 0 to exit levels <- unique(levels) # Ensure unique levelsEnter an item from the menu, or 0 to exit n <- length(levels) # Total number of levelsEnter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit if (!mode %in% c("simple", "medium", "extensive")) {Enter an item from the menu, or 0 to exit stop("Invalid mode. Choose 'simple', 'medium', or 'extensive'.")Enter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit contrasts <- c() # Store contrastsEnter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # 1. Pairwise comparisons (included in all modes, both A - B and B - A)Enter an item from the menu, or 0 to exit pairwise_contrasts <- combn(levels, 2, function(x) c(paste(x[1], "-", x[2]), paste(x[2], "-", x[1])), simplify = TRUE)Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit if (mode == "simple") {Enter an item from the menu, or 0 to exit return(unname(as.vector(pairwise_contrasts)))Enter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # 2. Comparisons against the mean of other groups (included in medium & extensive)Enter an item from the menu, or 0 to exit mean_contrasts <- unlist(lapply(levels, function(x) {Enter an item from the menu, or 0 to exit others <- setdiff(levels, x)Enter an item from the menu, or 0 to exit if (length(others) > 1) {Enter an item from the menu, or 0 to exit return(c(Enter an item from the menu, or 0 to exit paste(x, "- (", paste(others, collapse = " + "), ")/", length(others)),Enter an item from the menu, or 0 to exit paste("( ", paste(others, collapse = " + "), ")/", length(others), "-", x)Enter an item from the menu, or 0 to exit ))Enter an item from the menu, or 0 to exit } else {Enter an item from the menu, or 0 to exit return(c(paste(x, "-", others), paste(others, "-", x))) # Simple case: keep both ordersEnter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit }))Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit if (mode == "medium") {Enter an item from the menu, or 0 to exit return(unname(c(as.vector(pairwise_contrasts), mean_contrasts)))Enter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # 3. Groupwise comparisons (only included in extensive mode)Enter an item from the menu, or 0 to exit group_contrasts <- c()Enter an item from the menu, or 0 to exit for (i in 1:(n-1)) {Enter an item from the menu, or 0 to exit left_groups <- combn(levels, i, simplify = FALSE) # Subsets for the first groupEnter an item from the menu, or 0 to exit for (left in left_groups) {Enter an item from the menu, or 0 to exit right <- setdiff(levels, left) # Remaining elements for the second groupEnter an item from the menu, or 0 to exit if (length(right) > 1 && length(left) > 1) {Enter an item from the menu, or 0 to exit left_weighted <- paste0("(", paste(left, collapse = " + "), ")/", length(left))Enter an item from the menu, or 0 to exit right_weighted <- paste0("(", paste(right, collapse = " + "), ")/", length(right))Enter an item from the menu, or 0 to exit contrast1 <- paste(left_weighted, "-", right_weighted)Enter an item from the menu, or 0 to exit contrast2 <- paste(right_weighted, "-", left_weighted)Enter an item from the menu, or 0 to exit group_contrasts <- c(group_contrasts, contrast1, contrast2) # Keep both ordersEnter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit }Enter an item from the menu, or 0 to exit
Enter an item from the menu, or 0 to exit # Combine all contrasts and return as an unnamed vectorEnter an item from the menu, or 0 to exit contrasts <- unique(c(as.vector(pairwise_contrasts), mean_contrasts, group_contrasts)) # Remove duplicatesEnter an item from the menu, or 0 to exit return(unname(contrasts))Enter an item from the menu, or 0 to exit}Enter an item from the menu, or 0 to exit0
df_test$plot
NA
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